[12] Spatiotemporal Mathematical Framework for Financial Flow Tracking, Mule Network Detection & Emergency Asset Freeze Engine (AML Engine)
Year: 2026
Type: preprint
Abstract: Digital Financial Network Representation: Models transactions across bank accounts, digital wallets, and payment gateways as a time-weighted directed graph G=(V,E,W). Velocity Conservation Constraints: Eliminates implausible money laundering trajectories by enforcing processing velocity conservation principles where the total theoretical path duration equals the observed time interval (\sum W = \Delta T). Graph Laplacian & Dirichlet Energy: Applies the Graph Laplacian operator L=D-A and Dirichlet energy E(f) to detect localized topological energy perturbations caused by rapid micro-transactions across mule account networks. Bayesian Predictive Routing: Utilizes Bayes' Theorem to update posterior probabilities (P(E\vert{}\mathcal{D})) upon detecting anomalies at intermediate nodes, enabling the system to predict and intercept final cash-out or crypto endpoints. Real-Time Simulation Code: Accompanied by a functional Python simulation script (aml_engine_simulation.py) using NumPy and NetworkX to execute the core mathematical models and guide production integration.
Author: Pham, Minh Phuong
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225614354
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